R Notebook
library(Seurat)
library(SeuratDisk)
library(reticulate)
library(scrubletR)
library(ggplot2)
library(patchwork)
library(dplyr)
library(data.table)
library(clustree)
library(reshape2)
library(tidyr)
library(gridExtra)
Warning: package ‘gridExtra’ was built under R version 4.3.3
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combinelibrary(stringr)
library(plyr)---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Attaching package: ‘plyr’
The following object is masked from ‘package:purrr’:
compact
The following objects are masked from ‘package:dplyr’:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarizesource("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/tools/seurat_integration_functions.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/xgboost_train.R")
source("C:/Ryan/GitHub/trachtenberg-lab/transcriptomics/xgboost/plottingFxns.R")
obj.integrated <- IntegrateObjects(objs[[1]], objs[[2]], resolutions = c(1), nfeatures = 6000, subsample = TRUE)
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s Finding all pairwise anchors
| | 0 % ~calculating Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 21341 anchors
Filtering anchors
Retained 11098 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12m 38ssct.model: model1
Setting min_variance based on median UMI: 0.04
Calculating residuals of type pearson for 6805 genes
|
| | 0%
|
|======= | 4%
|
|============== | 7%
|
|===================== | 11%
|
|============================ | 15%
|
|=================================== | 19%
|
|========================================== | 22%
|
|================================================ | 26%
|
|======================================================= | 30%
|
|============================================================== | 33%
|
|===================================================================== | 37%
|
|============================================================================ | 41%
|
|=================================================================================== | 44%
|
|========================================================================================== | 48%
|
|================================================================================================= | 52%
|
|======================================================================================================== | 56%
|
|=============================================================================================================== | 59%
|
|====================================================================================================================== | 63%
|
|============================================================================================================================= | 67%
|
|==================================================================================================================================== | 70%
|
|=========================================================================================================================================== | 74%
|
|================================================================================================================================================= | 78%
|
|======================================================================================================================================================== | 81%
|
|=============================================================================================================================================================== | 85%
|
|====================================================================================================================================================================== | 89%
|
|============================================================================================================================================================================= | 93%
|
|==================================================================================================================================================================================== | 96%
|
|===========================================================================================================================================================================================| 100%sct.model: model1
Setting min_variance based on median UMI: 0.04
Calculating residuals of type pearson for 6805 genes
|
| | 0%
|
|======= | 4%
|
|============== | 7%
|
|===================== | 11%
|
|============================ | 15%
|
|=================================== | 19%
|
|========================================== | 22%
|
|================================================ | 26%
|
|======================================================= | 30%
|
|============================================================== | 33%
|
|===================================================================== | 37%
|
|============================================================================ | 41%
|
|=================================================================================== | 44%
|
|========================================================================================== | 48%
|
|================================================================================================= | 52%
|
|======================================================================================================== | 56%
|
|=============================================================================================================== | 59%
|
|====================================================================================================================== | 63%
|
|============================================================================================================================= | 67%
|
|==================================================================================================================================== | 70%
|
|=========================================================================================================================================== | 74%
|
|================================================================================================================================================= | 78%
|
|======================================================================================================================================================== | 81%
|
|=============================================================================================================================================================== | 85%
|
|====================================================================================================================================================================== | 89%
|
|============================================================================================================================================================================= | 93%
|
|==================================================================================================================================================================================== | 96%
|
|===========================================================================================================================================================================================| 100%Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: sparse->dense coercion: allocating vector of size 1.7 GiBWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n") :
NAs introduced by coercion to integer range
Warning in paste(prefix, message) :
NAs introduced by coercion to integer range
Warning: NAs introduced by coercion to integer range
subclasses <- c(c("L2/3", "L4", "L5IT", "L5NP", "L5PT", "L6CT", "L6IT", "L6b"), as.character(1:11))
PlotIntegration(obj.integrated, "species", c("integrated_snn_res.1"), subclasses)
objs.i <- SplitObject(obj.integrated, split.by = "species")
objs.m <- MapObjects(objs.i[[1]], objs.i[[2]], c("subclass", "type"), assay = "integrated")Warning: Both reference and query assays have been processed with SCTransform.Setting normalization.method = 'SCT' and continuing.Normalizing query using reference SCT model
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 2643 anchors
Filtering anchors
Retained 528 anchors
Warning: `invoke()` is deprecated as of rlang 0.4.0.Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
Predicting cell labels
| | 0 % ~calculating
Integrating dataset 2 with reference dataset
Finding integration vectors
Integrating data
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Computing nearest neighbors
Running UMAP projection
16:07:05 Read 10485 rows
16:07:05 Processing block 1 of 1
16:07:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:07:05 Initializing by weighted average of neighbor coordinates using 1 thread
16:07:05 Commencing optimization for 67 epochs, with 314550 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:07:08 Finished
Warning: Both reference and query assays have been processed with SCTransform.Setting normalization.method = 'SCT' and continuing.Normalizing query using reference SCT model
Projecting cell embeddings
Finding neighborhoods
Finding anchors
Found 1163 anchors
Filtering anchors
Retained 308 anchors
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
Predicting cell labels
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
| | 0 % ~calculating
Integrating dataset 2 with reference dataset
Finding integration vectors
Integrating data
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Computing nearest neighbors
Running UMAP projection
16:08:12 Read 10485 rows
16:08:12 Processing block 1 of 1
16:08:12 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:08:12 Initializing by weighted average of neighbor coordinates using 1 thread
16:08:12 Commencing optimization for 67 epochs, with 314550 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:08:17 Finished
PlotMapping(objs.m, ident.order = subclasses)
DimPlot(obj.mouse.glutamatergic.P38, group.by = "subclass", label = TRUE, raster = FALSE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
vfs <- VariableFeatures(obj.mouse.glutamatergic.P38[["SCT"]])
for (vf in 1:20) {
p <- FeaturePlot(obj.mouse.glutamatergic.P38, features = vfs[vf], max.cutoff = 10, raster = FALSE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
print(p)
}Scale for x is already present.
Adding another scale for x, which will replace the existing scale.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
DimPlot(obj.opossum.glutamatergic, group.by = "SCT_snn_res.1", label = TRUE, raster = FALSE) + NoLegend() + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
vfs <- VariableFeatures(obj.opossum.glutamatergic[["SCT"]])
for (vf in 21:40) {
p <- FeaturePlot(obj.opossum.glutamatergic, features = vfs[vf], max.cutoff = 10, raster = FALSE) + xlim(-18, 18) + ylim(-18, 18) + coord_equal()
print(p)
}Scale for x is already present.
Adding another scale for x, which will replace the existing scale.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
Idents(obj.opossum.glutamatergic) <- "SCT_snn_res.1"
obj.opossum.glutamatergic.IT <- subset(obj.opossum.glutamatergic, idents = c(2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19))
obj.opossum.glutamatergic.IT <- ClusterSCT(obj.opossum.glutamatergic.IT, resolutions = c(1))
PlotClusters(obj.opossum.glutamatergic.IT, group.id = "SCT_snn_res.1")Scale for x is already present.
Adding another scale for x, which will replace the existing scale.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Scale for x is already present.
Adding another scale for x, which will replace the existing scale.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
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